650 lines
411 KiB
Plaintext
650 lines
411 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "709c49bd",
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"metadata": {},
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"source": [
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"# Showcasing of AMBER utilizing synthetic neutron scattering data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d8b17015",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from AMBER.background import background\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "63aaf7ad",
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"metadata": {},
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"source": [
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"## Generate signal data\n",
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"The data for this tutorial is generated using the expected neutron scattering signal in an inelastic exmperiment measuring MnF$_2$. The dispersion relation, i.e. the energy at a give (H,K,L) position is given by the analytical formula [Yamany et al. 2010](https://www.doi.org/10.1139/P10-081):\n",
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"\n",
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"$$\\omega(H,K,L) = \\sqrt{\\left(2S z_2 J_2+D+2 z_1 S J_1 \\sin{L \\pi}^2\\right)^2-\\left(2 S z_2 J_2 \\cos(H \\pi)\\cos(K \\pi) \\cos(L \\pi)\\right)^2}$$\n",
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"\n",
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"In the above $J_1$, $J_2$, and $D$ are the magnetic coupling strengths and single-ion anisotropy which determine the amplitude of the dispersion. The parameters $S = 5/2$, $z_1 = 2$, and $z_2 = 8$ corresponds the the size of the magnetic spin, and the number of nearest and next-nearest neighbours in the MnF$_2$ lattice. \n",
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"\n",
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"From the above equation, the imporant fact is that it depends on the 3D vector $Q = (H,K,L)$. For simplicity, we assume that $K$ is zero and thus reduce the data to 3D, i.e. $(H,L,\\omega)$\n",
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"\n",
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"Due to how neutron scattering experiments function, the above dispersion will not be infinitely thin but rather extended. Here, this is replicated by a simple gaussian smearing along $\\omega$\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4c67ae0c-684b-4d85-92ba-44907594cce6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define parameters, dispersion, and Smearing\n",
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"S = 5/2\n",
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"z1 = 2\n",
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"z2 = 8\n",
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"\n",
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"def SpinWave(Q,J1,J2,D):\n",
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" return np.sqrt(np.power(2*S*z2*J2+D+2*z1*S*J1*np.sin(Q[2]*np.pi)**2,2.0)-\n",
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" \n",
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" np.power(2*S*z2*J2*np.cos(Q[0]*np.pi)*np.cos(Q[1]*np.pi)*np.cos(Q[2]*np.pi),2.0))\n",
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"\n",
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"def Intensity(H,K,L,E):\n",
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" sigmaE = 0.25\n",
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" omega = SpinWave(np.array([H,K,L]), J1=0.0354,J2=0.1499,D=0.131 )\n",
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" I = np.exp(-np.power(omega-E,2.0)/(2*sigmaE**2))\n",
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" \n",
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" return I"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b8cbd985",
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"metadata": {},
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"source": [
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"## Define 3D grid domain\n",
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"\n",
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"The input to AMBER is a 3D cube of intensities which is define below"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b1e107a4-306c-49dd-95d9-732ba4284c1e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data will ge simulated along H and L but with K = 0\n",
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"\n",
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"h = np.linspace(-0.1,2.1,101)\n",
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"k = 0\n",
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"l = np.linspace(-0.1,2.1,101)\n",
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"\n",
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"# Choose a sufficient energy range\n",
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"e = np.linspace(0.5,8,61)\n",
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"\n",
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"# Generate the grid upon which the dispersion is calculated\n",
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"\n",
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"H,L,E = np.meshgrid(h,l,e)\n",
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"K = np.zeros_like(H)\n",
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"\n",
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"# Intensities are calculated with the smearing and scaled\n",
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"I = 30*Intensity(H,K,L,E)\n",
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"\n",
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"# Poisson noice is added to the intensity\n",
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"I = np.random.poisson(I).astype(float)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c087a9d-e273-4be6-8151-87169990074b",
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"metadata": {},
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"source": [
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"### Introduction of NaN-values\n",
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"\n",
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"Triple axis instruments cannot measure all lengths of $Q$. this we can mimic by exchaning the intensity at these points by NaNs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9180b624-f76b-4d62-b87c-bda5388ef3f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"QLength = np.linalg.norm([H,L],axis=0)\n",
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"\n",
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"I[QLength<0.35] = np.nan\n",
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"I[QLength>2.5] = np.nan\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1bfcdd4-2525-44f9-9f75-3b9e81c2491f",
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"metadata": {},
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"source": [
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"### Add background\n",
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"The main feature of AMBER is the background segmentation, which requires the data to have a background. As describe in the [article](???), background is assumed to:\n",
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"\n",
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"- Rotation independence of the background.\n",
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"- Smooth change of background along energy and $|\\vec{Q}|$.\n",
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"- The signal is sparse but continuous in energy and $|\\vec{Q}|$.\n",
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"\n",
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"In the following, a background is generated with a higher amplitude for low and high $|\\vec{Q}|$ mimicing instrumental background artefacts from triple axis neutron experiments - these corresponds to the direct beam contribution and to increased background at larger scattering angles. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "99d07fc8-250c-4870-bfa8-a68dd212ad95",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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u7o5vvvlGbyEJGNKhfpzMycvFKK2qFTkNERGRYdB59+u9e/fC19f3pvIiCAIyMzPh6+sLmUyG6dOn6y0kAQGudgh0s8PlgnIcTS7E/eFeYkciIiISnc5nZNq1a4fCwsKb7i8uLkZAQIBeQtGt3dhEch9nLxEREQFoRpERBOGW95eVlcHa2vqeA9HtDb4+DfvgxXzUcZVfIiKipl9amj9/PgBAIpFg8eLFsLW1bXhMo9Hg5MmTiIyM1HtA+kt3fycobaxwraIWMenX0DPQRexIREREompykYmLiwNQf0bm3LlzkMlkDY/JZDJ07twZCxcu1H9CamBpIcWQUHdsisvC3sQ8FhkiIjJ7TS4yBw8eBAA8/vjj+Oijj6BQKFosFN3e8E4e2BSXhT2Jefjv6A6QSCRiRyIiIhKNzmNkVq9ezRIjon7BbpBZSpFRXIFLeWVixyEiIhJVk87ITJgwAWvWrIFCocCECRPu+NxNmzbpJRjdmp3cEv2CXLH/Yj72nM9FiKeD2JGIiIhE06QzMkqlsuEShlKpvOONWt6wjvWzl/ZyGjYREZk5iXC7+dQmQq1WQ6lUQqVSmcwlsYLSavR4ax8EAYheNBheShuxIxEREelVUz+/dR4jU1lZiYqKioav09PT8eGHH2LPnj3NS0o6c3OQo6uvEwBgXyLPyhARkfnSuciMHTsWa9euBQCUlJSgR48eWLFiBcaOHYtVq1bpPSDd2vDrl5f2sMgQEZEZ07nIxMbGol+/fgCAX375BZ6enkhPT8fatWvx8ccf6z0g3drwTp4AgOjUIqgquYkkERGZJ52LTEVFBRwc6mfK7NmzBxMmTIBUKkWvXr2Qnp6u94B0awGudghyt0edVsChpHyx4xAREYlC5yITFBSELVu2IDMzE7t378bw4cMBAPn5+SYzmNZY3Li8tJeXl4iIyEzpXGQWL16MhQsXwt/fHz179kRUVBSA+rMzXbp00XtAur0b07APJRWguk4jchoiIqLW1+QtCm546KGH0LdvX+Tk5KBz584N9w8ZMgTjx4/Xazi6s85tHeHuIEd+aTWiU4swMMRd7EhEREStSuczMgDg6emJLl26QCr969t79OiB0NBQvQWju5NKJRjeqf6szK6EXJHTEBERtT6di0x5eTleffVV9O7dG0FBQQgMDGx0o9Y1KswLALD7fC7qNFqR0xAREbUunS8tPfnkkzh8+DCmTp0KLy8v7r4ssp4BznCytcK1ilqcSitG7yBXsSMRERG1Gp2LzM6dO7Fjxw706dOnJfKQjiwtpBje0RMbz2RiZ0IuiwwREZkVnS8tOTk5wdnZuSWyUDONCq9fHG/X+VxotSa9dRYREVEjOheZN954A4sXL2603xKJq3c7VzhYW6KgtBoxGdfEjkNERNRqdL60tGLFCqSmpsLDwwP+/v6wsrJq9HhsbKzewlHTyCylGNbRA5tis/D7uRx09+cZMyIiMg86F5lx48a1QAy6V6PCvLApNgu7EnLx6uiOkEo5CJuIiEyfzkXmtddea4kcdI/6BbvCTmaBHFUV/rxagi6+TmJHIiIiM1BcXgNrKylsZTpXCr1o1k8tKSnBL7/8gtTUVLz44otwdnZGbGwsPDw80KZNG31npCawtrLA4A4e2PZnNnYl5LLIEBGR3hWUVuNUWjFOXynG+WwVUvLLcK2iFp9N6YrREV6iZNK5yJw9exZDhw6FUqnElStX8O9//xvOzs7YtGkTMjIysHbt2pbISU1wf5gntv2Zjd8TcvDyqFCu8UNERPcsR1WJrfHZ2H0+F3EZJbd9jlh0LjLz58/HjBkz8O6778LBwaHh/vvvvx9TpkzRazjSzYAQN1hbSZFZXInz2WqEtVGKHYmIiIyQIAg4dKkAa/+4gsOXCvD3lT06eCnQw98JXXydEORuj3Zu9rCRWYiWVecic/r0aXz55Zc33d+mTRvk5nK/HzHZyiwxKMQdOxNysTMhh0WGiIh0otUK2HY2G6sOpeJibmnD/T0CnDGmszeGdfCAp9JaxIQ307nIyOVyqNXqm+6/dOkS3Nzc9BKKmm9kmCd2JuTi93O5WDg8hJeXiIioSQ4l5eOdXUm4kFP/GW8ns8CjPXzxWC8/BLjaiZzu9nQuMg8++CBef/11/PTTTwAAiUSCjIwM/Oc//8HEiRP1HpB0M6SDB+SWUqQVlvPyEhER3dXVaxVYui0RexPzAAAOckv8u38gpkf5Q2lrdZfvFp/OK/uuWLECZWVlcHd3R2VlJQYMGICgoCA4ODjgzTffbImMpAN7uSWGdHAHAGw7my1yGiIiMlRarYBvjqVh6MrD2JuYB0upBDP7BuDIS4Mwd0iwUZQYoBlnZJRKJfbu3Yvjx4/jzz//RFlZGbp27YqhQ4e2RD5qhjER3vj9XC62/5mDl0dy9hIRETWWWVyBhT//iZNpxQCAngHOeGNcGNp7ONzlOw2PzkVm7dq1mDRpEvr06dNoB+yamhps2LAB06ZN02tA0t2gUHfYySyQVVKJ2IwSdPPjmjJERFRvV0IOXvzlLEqr6mArs8B/R3fAlB6+RvuPXp0vLT3++ONQqVQ33V9aWorHH39cL6Ho3lhbWWB4p/odsbfz8hIREQGo02ixdNt5zFoXi9KqOnT1dcSu5/vjsZ5+RltigGYUGUEQbvkHvnr1KpRK3QaWLl++HN27d4eDgwPc3d0xbtw4JCUlNXpOVVUVZs+eDRcXF9jb22PixInIy8vTNbbZGdO5foXFHWdzoPn7AgBERGR21FW1eHzNaaw+fgUA8PSAQGx8Ogq+LrbiBtODJl9a6tKlCyQSCSQSCYYMGQJLy7++VaPRIC0tDSNHjtTphx8+fBizZ89G9+7dUVdXh1deeQXDhw9HYmIi7Ozqp3q98MIL2LFjB37++WcolUrMmTMHEyZMwPHjx3X6Weamb5AblDZWyL++nHRUOxexIxERkQgyiyvwxJrTSM4vg42VBT6cHIkR18/am4ImF5kbu17Hx8djxIgRsLe3b3hMJpPB399f5+nXu3btavT1mjVr4O7ujpiYGPTv3x8qlQrffPMN1q9fj8GDBwMAVq9ejQ4dOuDEiRPo1auXTj/PnMgspRjZyRMbz2Ri29lsFhkiIjMUk16Mp9bGoKi8Bh4KOb6Z3t3kluVocpG5seu1v78/Jk2aBGtr/a/sd2PsjbOzMwAgJiYGtbW1jWZEhYaGwtfXF9HR0bcsMtXV1aiurm74+laL95mLMZ29sfFMJnaey8HSBzvBykLnK4lERGSkdiXkYO6P8ajRaBHWRoGvp3U3uFV59UHnWUvTp08HUD9LKT8/H1qtttHjvr6+zQqi1Woxb9489OnTB2FhYQCA3NxcyGQyODo6Nnquh4fHbbdDWL58OZYuXdqsDKamV6AzXO1lKCyrwfGUQgwMcRc7EhERtYKt8VmY/9Of0GgFDO/ogQ8nR8JWpvNHvlHQ+Z/oycnJ6NevH2xsbODn54eAgAAEBATA398fAQEBzQ4ye/ZsJCQkYMOGDc1+DQBYtGgRVCpVwy0zM/OeXs+YWVpIcX94/aDfbX/miJyGiIhawy8xV/HCxnhotAImdm2LVf/qZrIlBmjGGZkZM2bA0tIS27dvh5eXl16mbM2ZMwfbt2/HkSNH0LZt24b7PT09UVNTg5KSkkZnZfLy8uDpeeuBSnK5HHK5/J4zmYoxnb2xNjode87noqo2DNZW4u1QSkRELevHUxl4ZfM5CALwaA8fvDkuHFKp8U6tbgqdi0x8fDxiYmIQGhp6zz9cEAQ899xz2Lx5Mw4dOnTTGZ1u3brBysoK+/fvbxhInJSUhIyMDERFRd3zzzcH3Xyd4KW0Ro6qCoeS8jEyzEvsSERE1ALWRl/B4q3nAQDTo/yw5MFORr0+TFPpfGmpY8eOKCws1MsPnz17NtatW4f169fDwcEBubm5yM3NRWVlJYD67RBmzpyJ+fPn4+DBg4iJicHjjz+OqKgozlhqIqlUgjGdvQEAm+OyRE5DREQt4deYqw0l5t/9AsymxADNKDLvvPMOXnrpJRw6dAhFRUVQq9WNbrpYtWoVVCoVBg4cCC8vr4bbxo0bG57zwQcf4IEHHsDEiRPRv39/eHp6YtOmTbrGNmvju7QBABy4mI+SihqR0xARkT7tS8zDS7+eBQDM7BuAV+7vYDYlBgAkgiDotOyrVFrfff55kG6s+KvRaPSXTg/UajWUSiVUKhUUCoXYcUQz8sMjuJhbimXjwvCvXn5ixyEiIj04lVaMqd+cRHWdFhO6tMH7D3c2mTExTf381nmMzMGDB+8pGIljQtc2eOv3i9gcl8UiQ0RkAhKz1Zj53WlU12kxONQd7zwUYTIlRhc6F5kBAwa0RA5qYWMj2+DtnRcRk34N6UXl8HOxEzsSERE1U2ZxBaavPoXSqjp093fCZ1O6mu2ip00qMmfPnkVYWBikUinOnj17x+dGREToJRjpl4fCGn2CXHE0uRCb47Iwb2h7sSMREVEzqKtq8cSa0ygorUaopwO+nt4dNjLzXVqjSUUmMjISubm5cHd3R2RkJCQSCW41tMYQx8jQX8Z3adNQZJ4fEmxWg8GIiExBnUaL59bHITm/DB4KOdY83gNKGyuxY4mqSUUmLS0Nbm5uDf9NxmlEJ0/YWCUgvagCsRkl6ObnJHYkIiLSwVu/X8ThSwWwtpLif9PuM8m9k3TVpCLj5+d3y/8m42Int8TIME9sjsvClrgsFhkiIiPy46kMfHu8/mTCiocjEdHWUdxABsI8RwaZsRtrymw7m42aOu1dnk1ERIYgOrUIr25JAADMH9YeoyO4SvsNLDJmpk+QK9wd5CipqMWhpHyx4xAR0V1cvVaBZ3+IQZ1WwJjO3nhucJDYkQwKi4yZsZBKMDayfsuCTbHcsoCIyJBV12kw+4dYXKuoRXgbJd57KIITNf6BRcYMTehav8P4/ot5KC7nlgVERIbqje2J+POqCkobK3z+WFdYW5nvNOvbYZExQx28FAhvo0StRsAWbiRJRGSQNsddxboTGZBIgA8nR8LH2VbsSAapSbOWnJycmnwqq7i4+J4CUet45L62OJelwk9nMvF4H3+eqiQiMiAXc9VYtOkcAOC5wcEYFOIuciLD1aQi8+GHHzb8d1FREZYtW4YRI0YgKioKABAdHY3du3fj1VdfbZGQpH8PRrbBsh0XcDG3FOeyVJzGR0RkIEqravHMulhU1WrRL9gVzw8JFjuSQdN59+uJEydi0KBBmDNnTqP7P/30U+zbtw9btmzRZ757xt2vb+/5DXHYGp+Nx3r64s3x4WLHISIye4IgYO6GeGz7MxveSmtsn9sPznYysWOJoqmf3zqPkdm9ezdGjhx50/0jR47Evn37dH05EtGk+3wAAL/FZ6OyhltLEBGJ7ZeYq9j2ZzYspBJ8MqWr2ZYYXehcZFxcXLB169ab7t+6dStcXFz0EopaR69AF/g426C0ug67z+eKHYeIyKxdLijDa7+dB1C/6B1XX2+aJo2R+bulS5fiySefxKFDh9CzZ08AwMmTJ7Fr1y7873//03tAajlSqQQPd/PByr2XsPF0JsZdX/WXiIhaV3WdBnM3xKGiRoNegc6YNaCd2JGMhs5nZGbMmIHjx49DoVBg06ZN2LRpExQKBY4dO4YZM2a0QERqSRO7tYVEAkRfLkJGUYXYcYiIzNL7u5OQkKWGo60VPpzUBRZSziRtKp3PyABAz5498cMPP+g7C4mgjaMN+gW74cilAvwck4kFw0PEjkREZFYOJeXjf0frN4N8d2IEd7TWUbOKjFarRUpKCvLz86HVNt54sH///noJRq3nkfva4silAvwScxXzhrbnvwSIiFpJUVk1Fv58FgAwtZcfhnfyFDmR8dG5yJw4cQJTpkxBeno6/jlzWyKRQKPh7BdjM6yjBxxtrZCjqsKR5AIuvERE1AoEQcB/NyegsKwawe72+O/oDmJHMko6j5GZNWsW7rvvPiQkJKC4uBjXrl1ruHFVX+Mkt7TAhC71+y+tP5khchoiIvOwJT4Lu87nwlIqwQeTIrmPUjPpfEYmOTkZv/zyC4KCuI24KZnS0xffHk/D/gt5yC6phLejjdiRiIhMVnZJJRZvrZ9q/fyQYIS1UYqcyHjpfEamZ8+eSElJaYksJKIgd3tEBbpAKwAbTmeKHYeIyGRptQJe+uUsSqvq0NnHEc8M5FTre6HzGZnnnnsOCxYsQG5uLsLDw2FlZdXo8YiICL2Fo9b1WC9fRF8uwoZTGXhucBCsLLg5OhGRvq07mY5jKYWwtpJi5SOdYcnftfdE5yIzceJEAMATTzzRcJ9EIoEgCBzsa+SGd/SEq70c+aXV2JeYh1HhXmJHIiIyKZcLyvDW7xcAAItGdUA7N3uRExk/nYtMWlpaS+QgAyCzlGJS97b47GAqfjiZwSJDRKRHNy4pVdVq0TfIFVN7+YkdySToXGT8/HjgTdnk7r74/FAqjqUUIq2wHAGudmJHIiIyCd+fSMeZ9Guwk1ng7YnhkHLNLr3QucisXbv2jo9Pmzat2WFIfD7OthgU4o4DF/Ox/mQ6/ju6o9iRiIiMXmZxBd7ZdREA8PKoULR1shU5kemQCP9c1e4unJwa78ZZW1uLiooKyGQy2NraGtxaMmq1GkqlEiqVCgqFQuw4RmH/hTzM/O4MHG2tcGLREK5tQER0DwRBwLRvT+FociF6BDhjw7978WxMEzT181vnodJ/XwDv2rVrKCsrQ1JSEvr27Ysff/zxnkKTYRgY4o42jjYoqajFjrM5YschIjJqP8dcxdHkQsgtpXhnYgRLjJ7pZc5XcHAw3n77bTz//PP6eDkSmYVUgkd7+AConyZIRETNk6+uwrLtiQCA+cPac9xhC9Db5HVLS0tkZ2fr6+VIZJO6+8LKQoK4jBLEZ5aIHYeIyOgIgoD/25IAdVUdwtsoMbNvgNiRTJLOg31/++23Rl8LgoCcnBx8+umn6NOnj96CkbjcHOQYE+GNTXFZWH08DR9N7iJ2JCIio7LjXA72JObBUirBuw9FcOG7FqJzkRk3blyjryUSCdzc3DB48GCsWLFCX7nIADzeJwCb4rKw42wOXrm/AzwU1mJHIiIyCtfKa/Da9b2Unh0UhA5enGzSUnQuMlqttiVykAEKb6vEfX5OOJN+DetOpGPB8BCxIxERGYXXtyeiqLwG7T3sMWcQN1luSfd0nksQBOg4e5uMzON96q/prj+Zgapabj9BRHQ3hy8VYHNcFqQS4N2HOkNmyUtKLalZR3ft2rUIDw+HjY0NbGxsEBERge+//17f2cgAjOjkAW+lNYrKa/DbnxzMTUR0J1W1GizemgCg/h+CkT6O4gYyAzoXmZUrV+KZZ57B/fffj59++gk//fQTRo4ciVmzZuGDDz5oiYwkIksLKaZG+QMAVh+/wjNwRER38MXhVKQXVcBDIccLw9qLHccs6DxG5pNPPsGqVasabUXw4IMPolOnTliyZAleeOEFvQYk8T3awwcf7b+ECzlqnEwrRq9AF7EjEREZnCuF5fj8UCoAYPEDnWAv1/kjlppB5zMyOTk56N2790339+7dGzk5XAXWFDnayjCha1sAwOrj3P2ciOifBEHA4t/Oo6ZOi37Brrg/3FPsSGZD5yITFBSEn3766ab7N27ciODgYL2EIsPzeG9/AMDexDxkFleIG4aIyMDsTMjFkUsFkFlI8frYMEgk3Iagteh83mvp0qWYNGkSjhw50rAA3vHjx7F///5bFhwyDcEeDugX7IqjyYX49ngaXhvTSexIREQGoay6Dq9vq9+GYNbAdtyGoJXpfEZm4sSJOHXqFFxdXbFlyxZs2bIFrq6uOHXqFMaPH98SGclAPNU/EACw4VQmrpXXiJyGiMgwfLTvEnLVVfB1tsWzA9uJHcfs6HRGpra2Fk8//TReffVVrFu3rqUykYHqG+SKjl4KJOao8f2JdMwdwkuJRGTeLuaq8e3xKwCApWM7wdrKQtxAZkinMzJWVlb49ddfWyoLGTiJRIKnB9SflVnzxxUukEdEZk2rFfB/mxOg0QoY2ckTg0LcxY5klnS+tDRu3Dhs2bKlBaKQMRgd7oW2TjYoLq/BzzFXxY5DRCSaX2Ov4kz6NdjKLLB4TEex45gtnQf7BgcH4/XXX8fx48fRrVs32Nk1HtQ0d+5cvYUjw2NpIcW/+wXitd/O439HLmNKD19YSDk6n4jMS0lFDZbvvAgAeH5IMLwdbUROZL4kgo5LtQYEBNz+xSQSXL58ucmvdeTIEbz33nuIiYlBTk4ONm/e3Gh37RkzZuC7775r9D0jRozArl27mvwz1Go1lEolVCoVFAruPqoPFTV16PP2AVyrqMWnU7rggQhvsSMREbWqVzafw/qTGWjvYY8dc/vByoL7KelbUz+/dT4jk5amvwXRysvL0blzZzzxxBOYMGHCLZ8zcuRIrF69uuFruVyut59PzWMrs8S0KH98tD8ZXx6+jNHhXlwzgYjMRlzGNfx4KgMAsGxcOEuMyERdP3nUqFEYNWrUHZ8jl8vh6ckVEg3N9N7++PJIKs5lqRCdWoTeQa5iRyIianEarYD/25IAQQAmdm2LHgHOYkcyezoXmfnz59/yfolEAmtrawQFBWHs2LFwdtbP/9xDhw7B3d0dTk5OGDx4MJYtWwYXl9vv9VNdXY3q6uqGr9VqtV5yUGPOdjJMus8H30WnY9XhVBYZIjIL606k43y2GgprSyy6P1TsOIRmFJm4uDjExsZCo9EgJCQEAHDp0iVYWFggNDQUn3/+ORYsWIBjx46hY8d7G8U9cuRITJgwAQEBAUhNTcUrr7yCUaNGITo6GhYWt56rv3z5cixduvSefi41zZP9ArHuZAaOJhciPrOE29UTkUnLL63C+7uTAAAvjQyFqz2HOhgCnS/sjR07FkOHDkV2djZiYmIQExODq1evYtiwYXj00UeRlZWF/v3762UX7MmTJ+PBBx9EeHg4xo0bh+3bt+P06dM4dOjQbb9n0aJFUKlUDbfMzMx7zkG35uNsi/Fd2gAAPtmfLHIaIqKW9daOCyitrkPntko82sNX7Dh0nc5F5r333sMbb7zRaASxUqnEkiVL8O6778LW1haLFy9GTEyMXoMCQGBgIFxdXZGSknLb58jlcigUikY3ajmzBwVBKgH2X8xHQpZK7DhERC3ij9RCbInPhkRSP8CXy04YDp2LjEqlQn5+/k33FxQUNIxHcXR0RE2N/vfiuXr1KoqKiuDl5aX316bmCXC1w9jI+rMyH/OsDBGZoJo6LV7dkgAAmNrLD+FtlSInor9r1qWlJ554Aps3b8bVq1dx9epVbN68GTNnzmxYA+bUqVNo3779XV+rrKwM8fHxiI+PB1A/tTs+Ph4ZGRkoKyvDiy++iBMnTuDKlSvYv38/xo4di6CgIIwYMULX2NSCZg8KgkQC7EnMQ2I2B1cTkWn5+thlpBaUw9VehgXDQ8SOQ/+gc5H58ssvMWTIEEyePBl+fn7w8/PD5MmTMWTIEKxatQoAEBoaiq+//vqur3XmzBl06dIFXbp0AVA/I6pLly5YvHgxLCwscPbsWTz44INo3749Zs6ciW7duuHo0aNcS8bABLnbNyyK9+lBnpUhItNx9VpFw9nm/47uAKWNlciJ6J90Xtn3hrKysoZVfAMDA2Fvb6/XYPrClX1bx6W8Ugz/4AgAYPe8/gjxdBA5ERHRvfv32jPYm5iHngHO2PBULy7+2Yqa+vmt8xmZgwcPAgDs7e0RERGBiIiIhhLz2WefNTMuGbv2Hg4YFVa/cOGnB28/GJuIyFjsS8zD3sQ8WEolWDYujCXGQOlcZCZMmHDLGUkfffQRFi1apJdQZJzmDA4CAGw/m42U/FKR0xARNV9ljQav/XYeQP2aWcEePMtsqJo1/XrUqFG4ePFiw30rVqzA4sWLsWPHDr2GI+PSyVuJYR09IAjAB/s4VoaIjNenB5ORVVIJb6U15g4JEjsO3YHOK/s++eSTKC4uxtChQ3Hs2DFs3LgRb731Fn7//Xf06dOnJTKSEZk/rD32XcjDjrM5eGaACmFtOE2RiIxLSn4ZvjpSPwb0tQc7wVYm6raEdBfN+r/z0ksvoaioCPfddx80Gg12796NXr166TsbGaEOXgo82NkbW+Oz8f6eJKx5vIfYkYiImkwQBCzemoBajYDBoe4Y3tFD7Eh0F00qMh9//PFN97Vp0wa2trbo378/Tp06hVOnTgEA5s6dq9+EZHReGNoeO87m4FBSAU6lFXN3WCIyGr/9mY0/Uosgt5RiyZhOHOBrBJo0/TogIKBpLyaRNEzJNhScfi2OVzafw/qTGeju74Sfno7iLwMiMnjqqloMWXEYBaXVWDCsPZ4bEix2JLPW1M/vJp2RSUtL01swMg9zBwfjl5irOH3lGg5fKsDAEHexIxER3dHKPZdQUFqNAFc7PDUgUOw41EQ6z1oiagpPpTWmR/kBAN7bnQSttlnrLhIRtYqELBXWRl8BALw+thPklhbiBqIm07nITJw4Ee+8885N97/77rt4+OGH9RKKTMMzA4NgL7fE+Ww1dibkih2HiOiWtFoB/7clAVoBeCDCC/2C3cSORDrQucgcOXIE999//033jxo1CkeOHNFLKDINznYyPNmvfnzVij1JqNVoRU5ERHSzjWcyEZ9ZAnu5JV59oKPYcUhHOheZsrIyyGSym+63srKCWs2dj6mxmX0D4GInw+XCcqw/mSF2HCKiRorKqvH2zvoFXl8Y1h4eCmuRE5GudC4y4eHh2Lhx4033b9iwAR07sslSYw7WVpg3rD0A4MN9l6CqrBU5ERHRX97ZdRGqylqEejo0jOsj46LzgnivvvoqJkyYgNTUVAwePBgAsH//fvz444/4+eef9R6QjN+j3X3w3R9XkJJfhs8PpmDR/R3EjkREhDNXivHTmasAgDfHh8HSgvNfjJHO/9fGjBmDLVu2ICUlBc8++ywWLFiAq1evYt++fRg3blwLRCRjZ2khxX+vl5fVx68gs7hC5EREZO7qNFr835YEAMCk+3zQzY8LdxqrZm1RMHr0aIwePVrfWciEDQxxQ98gVxxLKcQ7uy7i0yldxY5ERGZszR9XcDG3FI62VvjPqFCx49A94Hk0ahUSiQSv3N8BEgmw/WwOYjOuiR2JiMxUrqoKH+y9BAB4eWQonO1unsBCxkPnIqPRaPD++++jR48e8PT0hLOzc6Mb0e109Fbg4W5tAQDLtieiCbtjEBHp3Rs7ElFeo0FXX0c8cp+P2HHoHulcZJYuXYqVK1di0qRJUKlUmD9/PiZMmACpVIolS5a0QEQyJQuGh8DGygKxGSXYdjZH7DhEZGaOXCrAjrM5kEqAZePCIZVyHzhjp3OR+eGHH/C///0PCxYsgKWlJR599FF8/fXXWLx4MU6cONESGcmEeCis8czAdgCAt3ZcQHl1nciJiMhcVNVqsHhr/QDfGb0D0NGbGwmbAp2LTG5uLsLDwwEA9vb2UKlUAIAHHngAO3bs0G86MklP9Q+Er7MtctVV+ORAithxiMhMfHn4Mq4UVcDdQY4XhnFna1Ohc5Fp27YtcnLqLwm0a9cOe/bsAQCcPn0acrlcv+nIJFlbWeC1MfWLJ35z7DJSC8pETkREpi69qByfHar/h9OrD3SEg7WVyIlIX3QuMuPHj8f+/fsBAM899xxeffVVBAcHY9q0aXjiiSf0HpBM05AOHhgc6o5ajYAlv53nwF8iajGCIODVredRU6dF3yBXPBDhJXYk0iOJcI+fINHR0YiOjkZwcDDGjBmjr1x6o1aroVQqoVKpoFDweqghSS8qx7CVR1Cj0eKLf3XFyDD+ciEi/dt+Nhtz1sdBZiHFrnn9EOhmL3YkaoKmfn43a0G8v4uKikJUVNS9vgyZIT8XOzw9IBCfHEjBG9svYEB7d9jILMSORUQmRF1Vi9e3JQIAnh3UjiXGBOl8aamoqKjhvzMzM7F48WK8+OKLOHr0qF6DkXl4dmAQ2jjaIKukEp8d5MBfItKvFbuTkF9ajQBXO8wa0E7sONQCmlxkzp07B39/f7i7uyM0NBTx8fHo3r07PvjgA3z11VcYNGgQtmzZ0oJRyRTZyCzw6gP1A3+/OnIZKfmlIiciIlNx9moJ1p5IBwC8MTYM1lY842uKmlxkXnrpJYSHh+PIkSMYOHAgHnjgAYwePRoqlQrXrl3D008/jbfffrsls5KJGtGpfuBvjUaLl389B62WA3+J6N5otAJe2XwOggCMi/RG32BXsSNRC2nyYF9XV1ccOHAAERERKCsrg0KhwOnTp9GtWzcAwMWLF9GrVy+UlJS0ZF6dcbCvccgqqcTwlYdRXqPBsnFh+FcvP7EjEZERW3M8DUu2JcLB2hIHFgyEmwOXBzE2Tf38bvIZmeLiYnh6egKoXwjPzs4OTk5ODY87OTmhtJSXBah52jjaYOGIEADAOzsvIk9dJXIiIjJWuaoqvL+nflPI/4wMZYkxcToN9pVIJHf8muheTIvyR2cfR5RW1+G1refFjkNERuqN7Ykoq65DF19HTOnhK3YcamE6Tb+eMWNGw+q9VVVVmDVrFuzs7AAA1dXV+k9HZsVCKsHbE8Ix5pNj2HU+F7vP52JEJ0+xYxGRETmYlI8d53JgIZXgTW4KaRaaXGSmT5/e6Ot//etfNz1n2rRp956IzFoHLwWe6h+Izw+lYvHWBES1c4GCS4kTURNU1vy1KeTjvf25KaSZaHKRWb16dUvmIGowd0gwfj+XgytFFXhrxwW8PTFC7EhEZAQ+PZiMzOJKeCmt8cKw9mLHoVai84J4RC3N2sqiobxsOJ2Jg0n5IiciIkOXnFeKr45cBgAsebAT7OT3vHA9GQkWGTJIvQJd8ESfAADAy7+ehaqiVuRERGSoBEHAf7ckoFYjYGgHdwzv6CF2JGpFLDJksF4aGYJAVzvkqauxZBtnMRHRrf185ipOpRXDxsoCSx7sxBm1ZoZFhgyWtZUF3n+kM6QSYHNcFnYl5IodiYgMTH5pFZbtqN8U8oVhwWjrZCtyImptLDJk0Lr6OuHp6xu9/XfzORSVcZo/Ef1lyW/noa6qQ3gbZcPlaDIvLDJk8OYNDUaIhwOKymvw380JaOKuGkRk4nafz8Xv53Lr16CaGA5LC36kmSP+XyeDJ7e0wIpHOsNSKsGu87nYeDpT7EhEJDJVZS1e3VK/ZszT/QPRyVspciISC4sMGYWwNsqGvZiWbktESn6ZyImISExv77yI/NJqBLjaYe6QYLHjkIhYZMhoPNUvEH2DXFFZq8HcH+NQXacROxIRieDE5SL8eCoDAPD2hHBYW1mInIjExCJDRkMqlWDlI53hbCdDYo4a7+xMEjsSEbWyqloNFm06BwCY0tMXPQNdRE5EYmORIaPirrDGew/Vr/r77fE0rvpLZGY+2p+MtMJyeCjkeHlUqNhxyACwyJDRGdLBAzN6+wMAFv70J/JLq8QNREStIiFL1bANwRtjw7ihLAFgkSEj9fKoUIR61k/JnvtjHOo0WrEjEVELqtNo8fKms9BoBdwf7onhnTzFjkQGgkWGjJK1lQU+ndIVdjILnLhcjBV7L4kdiYha0BeHU5GQpYbC2hJLHuwkdhwyICwyZLSC3O3xzvXxMqsOpWJvYp7IiYioJSRmq/HR/mQA9TtbuztYi5yIDImoRebIkSMYM2YMvL29IZFIsGXLlkaPC4KAxYsXw8vLCzY2Nhg6dCiSk5PFCUsG6YEI74bxMvN/ikdGUYW4gYhIr2rqtFj485+o1QgY1tED47u0ETsSGRhRi0x5eTk6d+6Mzz777JaPv/vuu/j444/xxRdf4OTJk7Czs8OIESNQVcXBnfSXV+7vgK6+jiitqsOsdTGoquX6MkSm4tODKUjMUcPR1gpvjg/jztZ0E1GLzKhRo7Bs2TKMHz/+pscEQcCHH36I//u//8PYsWMRERGBtWvXIjs7+6YzN2TeZJZSfPZY14b1ZRZv5X5MRKYgIUuFzw6mAKifpcRLSnQrBjtGJi0tDbm5uRg6dGjDfUqlEj179kR0dPRtv6+6uhpqtbrRjUyfl9IGH0/uAokE+OnMVXx/Il3sSER0D6rrNJj/Uzw0WgGjw70wprO32JHIQBlskcnNzQUAeHh4NLrfw8Oj4bFbWb58OZRKZcPNx8enRXOS4egb7IqXR9YvkLV0WyL+SCkUORERNdeH+5JxKa8MLnYyvD6Ws5To9gy2yDTXokWLoFKpGm6Zmdwp2Zw81T8Q47u0gUYr4Nn1sUgvKhc7EhHpKC7jGr48nAoAeHN8OFzs5SInIkNmsEXG07N+saO8vMZTavPy8hoeuxW5XA6FQtHoRuZDIpFg+YRwdPZxRElFLZ787gxKq2rFjkVETVRVq8GCn/+EVgDGRnpjZBgXvqM7M9giExAQAE9PT+zfv7/hPrVajZMnTyIqKkrEZGTorK0s8NXUbnB3kCM5vwwvbIyHVsvBv0TG4P3dSbhcUA43BzmWcuE7agJRi0xZWRni4+MRHx8PoH6Ab3x8PDIyMiCRSDBv3jwsW7YMv/32G86dO4dp06bB29sb48aNEzM2GQEPhTW+mnYfZJZS7LuQj7d3XRQ7EhHdxR+phfjmeBoA4O0J4XC0lYmciIyBpZg//MyZMxg0aFDD1/PnzwcATJ8+HWvWrMFLL72E8vJyPPXUUygpKUHfvn2xa9cuWFtzCh7dXaSPI957KALPb4jHV0cuw8fJBlOj/MWORUS3UFJRg/kb/4QgAI/28MGQDh53/yYiABLBxBfcUKvVUCqVUKlUHC9jpj49kIz391yCVAJ8NfU+DO3IX5BEhkQQBMxZH4cd53IQ4GqHHXP7wlYm6r+zyQA09fPbYMfIEOnL7EFBmNzdB1oBeO7HOJy9WiJ2JCL6m19js7DjXA4spRJ8OCmSJYZ0wiJDJk8ikeCNcWHo394NlbUaPLHmNDKLuScTkSHIKKrAa1sTAADzhgajs4+juIHI6LDIkFmwspDi88e6oqOXAoVlNZi++hSKyqrFjkVk1uo0WszbGIfyGg26+zvhmYFBYkciI8QiQ2bDXm6J1Y93h7fSGpcLyjFj9WmuMUMkoo8PpCA2owQOckusfCQSFlJuCEm6Y5Ehs+KhsMb3T/aEs50M57JUePK7M9wtm0gEf6QU4pMDyQCAZePD4ONsK3IiMlYsMmR22rnZY+0TPWAvt8TJtGLMWR+LWo1W7FhEZqOwrBrPb4yHIACT7vPB2Mg2YkciI8YiQ2YprI0SX0+/D/LrC+b955ezXP2XqBVotQLm//QnCkqrEexujyVcvZfuEYsMma1egS74/LGusJBKsCkuC/+3NQEmvqwSkei+PHIZRy4VwNpKis8e6wobmYXYkcjIsciQWRvSwQMrH+kMiQRYfzIDi7eeZ5khaiEx6dfw/p4kAMCSMZ3Q3sNB5ERkClhkyOyNjWyD9x6qLzPfn0jH0m2JLDNEelZcXoO5P8ZBoxUwprM3JnX3ETsSmQgWGSIAD3Vri3cmRAAA1vxxBW9sv8AyQ6QnGq2A5zfEIaukEv4utnhrfBgkEk61Jv1gkSG67pHuPnh7QjgA4NvjaXhzB8sMkT58uO8SjiYXwtpKii+mdoODtZXYkciEsMgQ/c3kHr54a3x9mfn6WBpe3ZrA2UxE92D/hTx8ciAFAPD2hAiEenLzXtIvFhmif5jS0xdvTwiHRAKsO5GBhT//iTquM0Oks4yiCrywMR4AMC3KD+O6cL0Y0j8WGaJbmNzDFx9OimyYmv3cj3GoqWOZIWqqqloNZq2LgbqqDl18HfF/ozuKHYlMFIsM0W2MjWyDVY91hcxCip0JuXjq+zOorOF2BkR3IwgCXv71LBJz1HCxk+Hzx7pCZsmPG2oZfGcR3cHwTp74ZsZ9sLaS4lBSAf71zUmUVNSIHYvIoH1x+DK2xGfDQirBJ1O6wEtpI3YkMmEsMkR30S/YDd/P7AmFtSVi0q9h4qo/cPVahdixiAzS/gt5eHf3RQDAkjEd0budq8iJyNSxyBA1QXd/Z/zyTG94Ka2RWlCOiav+wIUctdixiAxKcl4pnt9QvxnklJ6++FcvP7EjkRlgkSFqovYeDvj1md5o72GPPHU1HvkiGn+kFoodi8gglFTU4Mm1Z1BWXYeeAc5YMqYTF72jVsEiQ6QDb0cb/Px0b/Twd0ZpdR2mf3sKP5/JFDsWkahq6rR49odYpBdVoK2TDQf3UqviO41IR0pbK6yd2QOjw71QqxHw4i9n8fbOi1w4j8ySIAhYtOkc/kgtgq3MAv+bdh9c7OVixyIzwiJD1AzWVhb45NEueG5wEADgi8OpeOaHGFTU1ImcjKh1fbgvGb/GXoWFVILPHuuKDl5cuZdaF4sMUTNJpRIsGB6CDyZ1hsxCit3n8/DwF9HIUVWKHY2oVfx8JhMf7U8GALwxNgyDQtxFTkTmiEWG6B6N79IW6//dEy52MpzPVuOBj4/hxOUisWMRtahjyYVYtOkcAODZge0wpaevyInIXLHIEOnBff7O2DK7Dzp4KVBUXoPHvj6Jb46lcfdsMkmJ2Wo8sy4GdVoBYyO9sXB4iNiRyIyxyBDpiY+zLTY90xvjIr2h0Qp4Y3si5m2M57YGZFLSCssx7dtTKL0+zfrdhyIglXKaNYmHRYZIj2xkFvhgUiQWP9ARFlIJtsZnY/znx5FaUCZ2NKJ7lqOqxL++PonCsmp09FLgq2n3QW5pIXYsMnMsMkR6JpFI8ETfAPzwZE+42stwMbcUD35yDFvjs8SORtRsxeU1mPrNKWSVVCLA1Q7fPdEDShsrsWMRscgQtZRegS7YMbcfegU6o7xGg+c3xOPlX8+iqpaXmsi4lFbVYvq3p5CSXwYvpTXWPdkTbg5cK4YMA4sMUQvyUFjjhyd7Ye6QYEgkwIbTmRj76XEk55WKHY2oSSpq6jDzuzM4l6WCs50M38/siTaO3M2aDAeLDFELs5BKMH9Ye6yb2ROu9nIk5ZXigU+OYc1xzmoiw1ZRU4fHV5/GqbRiOMgtsfaJHghytxc7FlEjLDJEraRPkCt2Pt8PA0PcUF2nxZJtiZi++jTy1VViRyO6yY0Sc/J6ifluZg+EtVGKHYvoJiwyRK3IzUGO1TO6442xnSC3lOLIpQKM+PAIdiXkiB2NqEFFTR1m/KPEdPV1EjsW0S2xyBC1MolEgqlR/tgxty86eStwraIWs9bFYvb6WBSWVYsdj8xceXV9iWm4nMQSQwaORYZIJEHuDtj8bB/MHtQOFlIJdpzNwbCVh7ElLotjZ0gU166vSv33EtOFJYYMHIsMkYhkllK8OCIUW69vb3CtohbzNsZj5ndnuPkktapcVRUe+TIa8ZklcLS1wvdP9mSJIaPAIkNkAMLaKPHbnD5YMKw9ZBZSHLiYj+Erj+DHUxk8O0Mt7nJBGSau+gPJ+WXwUMjx09NRiPRxFDsWUZOwyBAZCCsLKZ4bEowdc/si0scRpdV1WLTpHCZ9dQIXc9VixyMTlZClwsNfRDes2PvLrN5o7+EgdiyiJpMIJv7PPbVaDaVSCZVKBYVCIXYcoibRaAWsPp6G9/ckoapWCwupBNOi/PDCsPZQWHNZeNKPgxfzMWd9LMprNOjkrcB3T/SAqz1X7CXD0NTPbxYZIgN29VoF3txxATsTcgEArvYyvDyqAyZ0acMdh+merDmehte3J0IrAFGBLvhyWjeWZDIoLDLXsciQKThyqQBLtp3H5YJyAEA3Pye8PrYTOnlzgTLSTZ1Gi9e3J2JtdDoA4JH72mLZuHDILDnSgAwLi8x1LDJkKmrqtPj2eBo+3p+MihoNpBLgkft88MKw9vBQWIsdj4yAuqoWz62Pw+FLBQCAl0eF4un+gZBIeHaPDA+LzHUsMmRqclSVeOv3i9j2ZzYAwNpKipl9A/D0gHa8NEC3dSFHjWfWxeBKUQWsraT4cFIkRoZ5iR2L6LZYZK5jkSFTFZNejOW/X8SZ9GsAACdbKzw3OBj/6uXHywTUyKbYq3hl8zlU1WrRxtEGX/yrG8Lb8rIkGTYWmetYZMiUCYKAvYl5eGfXRaReHz/j42yDhcNDMCbCmwOCzVx1nQavb0vEDyczAAD9gl3x0eQucLaTiZyM6O5YZK5jkSFzUKfR4qczV/HBvksoKK3frynY3R7PDQnG6HAvWLDQmJ3LBWWYtzEeZ6+qAABzhwTj+SHBfC+Q0WCRuY5FhsxJRU0dvjmahq+OXkZpVR0AoJ2bHZ4bHIwHIrxgacFLTqZOEAT8eCoTb2xPRGWtBkobK3w4KRKDQt3FjkakExaZ61hkyBypq2qx5vgVfHMsDarKWgBAgKsd5gwKwthIbxYaE1VYVo3//HIW+y/mAwB6t3PBikc6w0tpI3IyIt2ZRJFZsmQJli5d2ui+kJAQXLx4scmvwSJD5qy0qhZro9Pxv6OXUVJRX2h8nW3xRB9/PHyfD+zkliInJH0QBAG7EnLx6tYEFJbVQGYhxUsjQ/BEnwCOkyKj1dTPb4P/LdapUyfs27ev4WtLS4OPTGQwHKytMHtQEKb39sf31wtNRnEFlmxLxMq9lzClpx9m9PaHp5Lr0Bir7JJKLN6agH0X6s/ChHg44MPJkejgxX+4kXkw+FZgaWkJT09PsWMQGTV7uSWeGdgO03v74dfYLHx7LA1pheX44nAqvj56GWM6e+PJfgFcKdiIaLQCvvvjClbsSUJ5jQZWFhI8M6Adnh0UBGsrC7HjEbUagy8yycnJ8Pb2hrW1NaKiorB8+XL4+vre9vnV1dWorq5u+Fqt5q7BRDfYyiwxtZcfHuvhi30X8vD1sTScSivG5rgsbI7LQo8AZzzW0xcjwzwht+SHoaE6lVaMN7Yn4lxW/Yyk+/ycsHxCOIK5azWZIYMeI7Nz506UlZUhJCQEOTk5WLp0KbKyspCQkAAHh1v/hb3VuBoAHCNDdBtnr5bg66Np2HEuBxpt/a8DZzsZHurWFo/28EWAq53ICemGK4XleHvnRew6X7+JqIPcEi/fH4pHu/tyLAyZHJMY7PtPJSUl8PPzw8qVKzFz5sxbPudWZ2R8fHxYZIjuIkdViY2nM7HxdCZyVFUN9/du54IpPX0xrKMHz9KI5Fp5DT47mILvoq+gViNAKgEe7eGLF4a1h6u9XOx4RC3CZAb7/p2joyPat2+PlJSU2z5HLpdDLudfbCJdeSltMG9oe8wZFIRDSQVYfyoDB5Py8UdqEf5ILYLSxgr3h3thfJc2uM/PiWcAWkFxeQ3+d/Qy1v5xBeU1GgBA//Zu+O/9HRDiyctIRICRFZmysjKkpqZi6tSpYkchMlmWFlIM7eiBoR09kFVSiY2nMvDTmavIVVfhx1MZ+PFUBto42mBspDfGd2nDcRktoKisGl8dvYzvo9NRcb3AdPRS4MWRIRgUwoXtiP7OoC8tLVy4EGPGjIGfnx+ys7Px2muvIT4+HomJiXBzc2vSa3AdGaJ7p9EKOHm5CJvjsrAzIRdl1XUNj3X0UmBUmCdGhHki2N0eEgnP1DTXhRw11hy/gi3xWaiu0wIAwtoo8PyQ9hjawZ3HlsyKSYyRmTx5Mo4cOYKioiK4ubmhb9++ePPNN9GuXbsmvwaLDJF+VdVqsP9CPjbHZeFQUj7qtH/9CglwtcPwjh4Y3skTXXwcefmpCeo0Wuy7kI/Vx9NwMq244f7ObZWYOyQYg0NZYMg8mUSR0QcWGaKWU1xegz3nc7EnMQ/HkgtRo9E2PObuIMeQDh7oH+yK3u1cobS1EjGp4UnMVuPX2KvYGp+FwrIaAICFVIKRYZ54oo8/uvo6scCQWWORuY5Fhqh1lFXX4VBSPnafz8PBi/mNLj9JJUBEW0f0C3ZF3yBXdPF1gszS/PZ7ulJYjj2Judgcl40LOX+tceViJ8Ok7j74Vy8/eDtyXyQigEWmAYsMUeurrtMgOrUIh5IKcCylECn5ZY0et5NZoJu/M7r5OqGbnxMifR1hb4L7Pmm1As5lqbA3MQ97EnNxKe+v4yCzkGJIB3dM7NoWA0LcYMWNPIkaYZG5jkWGSHw5qkocTS7EseRCHEspRHF5TaPHpRIgxFOBrr6O6ObnhLA2SgS42hndh7sgCEjOL8MfKYWIvlyEk2nFDZt1AoClVIKegc4YGeaFMRFecLSViZiWyLCxyFzHIkNkWLRaARdy1Thz5RpiM64hJv0arl6rvOl5Mgsp2rnbI9TTAaGeDgjxdECopwIeCrlBjB0RBAFZJZVIyFLh7FUVzmXV3/5eXID6fa4GtHfDsI4eGBTizrFCRE3EInMdiwyR4ctTVyE2vb7YxGaUICm3tNEYm7+ztpLC19kWvs528HOxRRtHG3gorOGhkMNDYQ03B7leNk0UBAHqqjrkq6uQq65CnroaGUXluFxYjssF5UgrLEdlreaW+br7O6NXoAui2rkgvI3S6M4sERkCFpnrWGSIjI8gCLh6rRIXc0uRlKvGhdxSJOWWIq2wvGE/qDuRW0qhsLGC0sYKCmtL2MktYWUhhcxCCpmlFJYWEmi1AjQCoNFqodEKqKjRoLSqDmXVdSitqoWqshZVtdo7/hxLqQQhng6IaKtEWBslIto4IsTTwSwHMhPpm0luUUBE5kEikcDH2RY+zrYY1tGj4f5ajRZZ1yqRXlyBjKJypBdVIEddhfzrZ0zy1FWortOiuk6LgtJqFJRW3+GnNI3SxqrhbE9bJxsEutojwNUOgW528HG25dkWIpGxyBCR0bCykMLf1Q7+rnYAbl7d+8blIHVl/RkVdVUt1JW1qKzVoKZOi5rrJadOK8BCIoGFVAJLCwmkEglsrCzgYG0Je2tLKKytoLC2gpuDHDYybpRJZMhYZIjIZEgkEiivX1LyETsMEbUKnhMlIiIio8UiQ0REREaLRYaIiIiMFosMERERGS0WGSIiIjJaLDJERERktFhkiIiIyGixyBAREZHRYpEhIiIio8UiQ0REREaLRYaIiIiMFosMERERGS0WGSIiIjJaJr/7tSAIAAC1Wi1yEiIiImqqG5/bNz7Hb8fki0xpaSkAwMfHR+QkREREpKvS0lIolcrbPi4R7lZ1jJxWq0V2djYcHBwgkUjEjtPq1Go1fHx8kJmZCYVCIXYco8ZjqT88lvrDY6kfPI76o69jKQgCSktL4e3tDan09iNhTP6MjFQqRdu2bcWOITqFQsG/nHrCY6k/PJb6w2OpHzyO+qOPY3mnMzE3cLAvERERGS0WGSIiIjJaLDImTi6X47XXXoNcLhc7itHjsdQfHkv94bHUDx5H/WntY2nyg32JiIjIdPGMDBERERktFhkiIiIyWiwyREREZLRYZIiIiMhosciYgM8++wz+/v6wtrZGz549cerUqds+d82aNZBIJI1u1tbWrZjWMB05cgRjxoyBt7c3JBIJtmzZctfvOXToELp27Qq5XI6goCCsWbOmxXMaA12P5aFDh256T0okEuTm5rZOYAO2fPlydO/eHQ4ODnB3d8e4ceOQlJR01+/7+eefERoaCmtra4SHh+P3339vhbSGrTnHkr8vb7Zq1SpEREQ0LHYXFRWFnTt33vF7Wvr9yCJj5DZu3Ij58+fjtddeQ2xsLDp37owRI0YgPz//tt+jUCiQk5PTcEtPT2/FxIapvLwcnTt3xmeffdak56elpWH06NEYNGgQ4uPjMW/ePDz55JPYvXt3Cyc1fLoeyxuSkpIavS/d3d1bKKHxOHz4MGbPno0TJ05g7969qK2txfDhw1FeXn7b7/njjz/w6KOPYubMmYiLi8O4ceMwbtw4JCQktGJyw9OcYwnw9+U/tW3bFm+//TZiYmJw5swZDB48GGPHjsX58+dv+fxWeT8KZNR69OghzJ49u+FrjUYjeHt7C8uXL7/l81evXi0olcpWSmecAAibN2++43NeeukloVOnTo3umzRpkjBixIgWTGZ8mnIsDx48KAAQrl271iqZjFl+fr4AQDh8+PBtn/PII48Io0ePbnRfz549haeffrql4xmVphxL/r5sGicnJ+Hrr7++5WOt8X7kGRkjVlNTg5iYGAwdOrThPqlUiqFDhyI6Ovq231dWVgY/Pz/4+PjcsUnT7UVHRzc67gAwYsSIOx53urPIyEh4eXlh2LBhOH78uNhxDJJKpQIAODs73/Y5fG82TVOOJcDfl3ei0WiwYcMGlJeXIyoq6pbPaY33I4uMESssLIRGo4GHh0ej+z08PG47viAkJATffvsttm7dinXr1kGr1aJ37964evVqa0Q2Gbm5ubc87mq1GpWVlSKlMk5eXl744osv8Ouvv+LXX3+Fj48PBg4ciNjYWLGjGRStVot58+ahT58+CAsLu+3zbvfe5JijvzT1WPL35a2dO3cO9vb2kMvlmDVrFjZv3oyOHTve8rmt8X40+d2vqbGoqKhGzbl3797o0KEDvvzyS7zxxhsiJiNzFRISgpCQkIave/fujdTUVHzwwQf4/vvvRUxmWGbPno2EhAQcO3ZM7ChGr6nHkr8vby0kJATx8fFQqVT45ZdfMH36dBw+fPi2Zaal8YyMEXN1dYWFhQXy8vIa3Z+XlwdPT88mvYaVlRW6dOmClJSUlohosjw9PW953BUKBWxsbERKZTp69OjB9+TfzJkzB9u3b8fBgwfRtm3bOz73du/Npv5OMHW6HMt/4u/LejKZDEFBQejWrRuWL1+Ozp0746OPPrrlc1vj/cgiY8RkMhm6deuG/fv3N9yn1Wqxf//+216v/CeNRoNz587By8urpWKapKioqEbHHQD27t3b5ONOdxYfH8/3JABBEDBnzhxs3rwZBw4cQEBAwF2/h+/NW2vOsfwn/r68Na1Wi+rq6ls+1irvR70NGyZRbNiwQZDL5cKaNWuExMRE4amnnhIcHR2F3NxcQRAEYerUqcLLL7/c8PylS5cKu3fvFlJTU4WYmBhh8uTJgrW1tXD+/Hmx/ggGobS0VIiLixPi4uIEAMLKlSuFuLg4IT09XRAEQXj55ZeFqVOnNjz/8uXLgq2trfDiiy8KFy5cED777DPBwsJC2LVrl1h/BIOh67H84IMPhC1btgjJycnCuXPnhOeff16QSqXCvn37xPojGIxnnnlGUCqVwqFDh4ScnJyGW0VFRcNz/vl3/Pjx44KlpaXw/vvvCxcuXBBee+01wcrKSjh37pwYfwSD0Zxjyd+XN3v55ZeFw4cPC2lpacLZs2eFl19+WZBIJMKePXsEQRDn/cgiYwI++eQTwdfXV5DJZEKPHj2EEydONDw2YMAAYfr06Q1fz5s3r+G5Hh4ewv333y/ExsaKkNqw3JgC/M/bjWM3ffp0YcCAATd9T2RkpCCTyYTAwEBh9erVrZ7bEOl6LN955x2hXbt2grW1teDs7CwMHDhQOHDggDjhDcytjiOARu+1f/4dFwRB+Omnn4T27dsLMplM6NSpk7Bjx47WDW6AmnMs+fvyZk888YTg5+cnyGQywc3NTRgyZEhDiREEcd6PEkEQBP2d3yEiIiJqPRwjQ0REREaLRYaIiIiMFosMERERGS0WGSIiIjJaLDJERERktFhkiIiIyGixyBAREZHRYpEhIiIio8UiQ0QG7dChQ/D392/W9w4cOBBr1qzRax4iMiwsMkRkdLZv344BAwbAwcEBtra26N69OwsLkZlikSEio/LJJ59g7Nix6NOnD06ePImzZ89i8uTJmDVrFhYuXCh2PCJqZZZiByAiaqrMzEwsWLAA8+bNw1tvvdVw/4IFCyCTyTB37lw8/PDD6Nmzp4gpiag18YwMERmNX375BbW1tbc88/L000/D3t4eP/74owjJiEgsLDJEZDQuXboEpVIJLy+vmx6TyWQIDAzEpUuXREhGRGJhkSEikyKTycSOQEStiEWGiIxGcHAwVCoVsrOzb3qspqYGqampaN++vQjJiEgsLDJEZDQeeughWFpaYsWKFTc99sUXX6CiogLTpk0TIRkRiYWzlojIaPj6+uLdd9/FwoULYW1tjalTp8LKygpbt27FK6+8gmXLliEsLEzsmETUilhkiMiovPDCCwgMDMSKFSvw0Ucfoby8HADw448/YvLkySKnI6LWxktLRGR0xo4diyNHjqCsrAxFRUWIjIzEqlWrUFFRIXY0ImplLDJEZNScnZ2xb98+DBkyBNHR0WLHIaJWxktLRGT0XFxcsHjxYrFjEJEIeEaGiAyav78/5s2b16zvnTFjBiIjI/Wah4gMi0QQBEHsEERERETNwTMyREREZLRYZIiIiMhoscgQERGR0WKRISIiIqPFIkNERERGi0WGiIiIjBaLDBERERktFhkiIiIyWv8P7CEgmJcp5aIAAAAASUVORK5CYII=",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Background definition\n",
|
|
"def background_simulation(q,amplitude,gamma,mu,amplitude2,gamma2):\n",
|
|
" return amplitude*((gamma/(q**2+gamma**2))+amplitude2*np.exp(-np.power(q-mu,2.0)/(2*gamma2**2)))\n",
|
|
"\n",
|
|
"\n",
|
|
"Q = np.linalg.norm([H,K,L],axis=0)\n",
|
|
"\n",
|
|
"# Choose suitable valies\n",
|
|
"gamma = 0.5\n",
|
|
"mu = 3.0\n",
|
|
"amplitude = 20\n",
|
|
"amplitude2 = 1.0\n",
|
|
"gamma2 = 0.5\n",
|
|
"\n",
|
|
"# Generate an example of the background for visual inspection\n",
|
|
"q = np.linspace(0.25,Q.max(),201)\n",
|
|
"bg_test = background_simulation(q,amplitude,gamma,mu,amplitude2,gamma2)\n",
|
|
"\n",
|
|
"# display background amplitude\n",
|
|
"fig,ax = plt.subplots()\n",
|
|
"ax.plot(q,bg_test)\n",
|
|
"ax.set_xlabel('|Q|')\n",
|
|
"ax.set_ylabel('Background intensity')\n",
|
|
"\n",
|
|
"\n",
|
|
"## Add background to data\n",
|
|
"bg_tmp = background_simulation(Q,amplitude,gamma,mu,amplitude2,gamma2)\n",
|
|
"\n",
|
|
"I+=np.random.poisson(bg_tmp)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0d7f0e5e-6f8f-450d-b1ff-afce5699f64a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plot a constant energy plot\n",
|
|
"The now background affected data is plotted to show a \"before\" picture. This is done by choosing a specific energy ($\\omega \\sim 4.5$ meV) and plotting the intensity as a color map"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "456b2e68-9d32-4e78-9575-e6a2bcf7e9e5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, 'L [r.l.u.]')"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig,ax = plt.subplots()\n",
|
|
"\n",
|
|
"# Find the closest energy slice\n",
|
|
"energy = 4.5\n",
|
|
"EIdx = np.argmin(np.abs(E-energy))\n",
|
|
"ax.pcolormesh(H[:, :, EIdx], L[:, :, EIdx], I[:, :, EIdx], vmin=0, vmax = 50)\n",
|
|
"\n",
|
|
"ax.axis('equal')\n",
|
|
"ax.set_title('E = {:.2f} meV'.format(e[EIdx]))\n",
|
|
"ax.set_xlabel('H [r.l.u.]')\n",
|
|
"ax.set_ylabel('L [r.l.u.]')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "19859951-3686-46a3-b11e-0cc03b5e97fc",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plot a constant H map"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "5484ba62-d027-408b-9e34-e0f0e0ab4528",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, 'E [meV]')"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig,ax = plt.subplots()\n",
|
|
"\n",
|
|
"h_value = 0.25\n",
|
|
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"ax.pcolormesh(L[:, HIdx, :],E[:, HIdx, :],I[:, HIdx, :], vmin=0, vmax=50)\n",
|
|
"\n",
|
|
"ax.set_xlabel('L [r.l.u.]')\n",
|
|
"ax.set_ylabel('E [meV]')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2f7f37f2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Run denoising algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "3a58ab49",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Initialize the AMBER background object\n",
|
|
"\n",
|
|
"AMBER = background(dtype=np.float32)\n",
|
|
"\n",
|
|
"# Set the grid sizes\n",
|
|
"AMBER.set_gridcell_size(dqx = 0.022, dqy = 0.022, dE = 0.125)\n",
|
|
"\n",
|
|
"# Alternatively these can be set from the h, l, and e arrays like\n",
|
|
"# AMBER.set_volume_from_limits([h[0],l[0],e[0]],[h[-1],l[-1],e[-1]],)\n",
|
|
"\n",
|
|
"# Input the data\n",
|
|
"AMBER.set_binned_data(h, l, e, I)\n",
|
|
"\n",
|
|
"bins = int((q.max()-q.min())/0.022)\n",
|
|
"\n",
|
|
"# define maximum radius and number of bins\n",
|
|
"AMBER.set_radial_bins(q.max(),n_bins=bins)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cfb11870",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Set algorithm parameters ($\\lambda, \\beta, \\mu$)\n",
|
|
"\n",
|
|
"lambda and mu will be determined as described in the paper and we select beta using cross validation\n",
|
|
"\n",
|
|
"beta_ is selected using cross validation, i.e. mask out the top q quantile of intensity. The beta value for the lowest Root-Mean-Square-Error is then chosen"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "60e2fbf2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Test - ( 0.1 )\n",
|
|
"RMSE - ( 4.4478 0.1 55.50269024150275 ) : 3.486001\n",
|
|
"Test - ( 1.0 )\n",
|
|
"RMSE - ( 4.4478 1.0 55.50269024150275 ) : 3.4850485\n",
|
|
"Test - ( 10.0 )\n",
|
|
"RMSE - ( 4.4478 10.0 55.50269024150275 ) : 3.47741\n",
|
|
"Test - ( 100.0 )\n",
|
|
"RMSE - ( 4.4478 100.0 55.50269024150275 ) : 3.4501917\n",
|
|
"Test - ( 200.0 )\n",
|
|
"RMSE - ( 4.4478 200.0 55.50269024150275 ) : 3.4432225\n",
|
|
"Test - ( 300.0 )\n",
|
|
"RMSE - ( 4.4478 300.0 55.50269024150275 ) : 3.4426384\n",
|
|
"Test - ( 400.0 )\n",
|
|
"RMSE - ( 4.4478 400.0 55.50269024150275 ) : 3.4448829\n",
|
|
"Test - ( 500.0 )\n",
|
|
"RMSE - ( 4.4478 500.0 55.50269024150275 ) : 3.4486284\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"lambda_tmp = AMBER.MAD_lambda()\n",
|
|
"mu_tmp = AMBER.mu_estimator()\n",
|
|
"\n",
|
|
"\n",
|
|
"beta_range_tmp = np.array([0.1,1.0,10.0,100.0,200.0,300.0,400.0,500.0])\n",
|
|
"rmse = AMBER.cross_validation(q=0.3,beta_range= beta_range_tmp, lambda_=lambda_tmp, mu_=mu_tmp,n_epochs=15,verbose=False)\n",
|
|
"\n",
|
|
"\n",
|
|
"beta_tmp = beta_range_tmp[np.argmin(rmse)]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e81bce2a",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Run the denoising algorithm using the parameters obtained using the heuristic"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "1f9a8679",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Iteration 1\n",
|
|
" Loss function: 24616092.0\n",
|
|
" Iteration 2\n",
|
|
" Loss function: 18311960.0\n",
|
|
" Iteration 3\n",
|
|
" Loss function: 15576615.0\n",
|
|
" Iteration 4\n",
|
|
" Loss function: 14319358.0\n",
|
|
" Iteration 5\n",
|
|
" Loss function: 13718133.0\n",
|
|
" Iteration 6\n",
|
|
" Loss function: 13445899.0\n",
|
|
" Iteration 7\n",
|
|
" Loss function: 13333092.0\n",
|
|
" Iteration 8\n",
|
|
" Loss function: 13288917.0\n",
|
|
" Iteration 9\n",
|
|
" Loss function: 13271952.0\n",
|
|
" Iteration 10\n",
|
|
" Loss function: 13265445.0\n",
|
|
" Iteration 11\n",
|
|
" Loss function: 13262938.0\n",
|
|
" Iteration 12\n",
|
|
" Loss function: 13261957.0\n",
|
|
" Iteration 13\n",
|
|
" Loss function: 13261567.0\n",
|
|
" Iteration 14\n",
|
|
" Loss function: 13261418.0\n",
|
|
" Iteration 15\n",
|
|
" Loss function: 13261355.0\n",
|
|
" Iteration 16\n",
|
|
" Loss function: 13261332.0\n",
|
|
" Iteration 17\n",
|
|
" Loss function: 13261326.0\n",
|
|
" Iteration 18\n",
|
|
" Loss function: 13261322.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# set number of epochs\n",
|
|
"n_epochs = 20\n",
|
|
"\n",
|
|
"AMBER.denoising(AMBER.Ygrid,lambda_tmp,beta_tmp,mu_tmp,n_epochs,verbose=True) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e909ad23",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Compute substracted signal"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "15d23f11",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# The subtracted signal is given by\n",
|
|
"\n",
|
|
"Y_sub = AMBER.Ygrid - AMBER.b_grid\n",
|
|
"Y_back = AMBER.b_grid\n",
|
|
"\n",
|
|
"# reshape the data to fit \n",
|
|
"Y_sub = Y_sub.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n",
|
|
"Y_back = Y_back.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n",
|
|
"\n",
|
|
"# reshape the observation\n",
|
|
"Y_obs = AMBER.Ygrid.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bf1032b5",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Display substracted signal"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "e9e74383",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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"text/plain": [
|
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"<Figure size 2000x900 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# reshape the observation\n",
|
|
"Y_obs = AMBER.Ygrid.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n",
|
|
"\n",
|
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"\n",
|
|
"fig0 = plt.figure(figsize=(20, 9))\n",
|
|
"\n",
|
|
"# Plot 1: Observations Y\n",
|
|
"ax0 = fig0.add_subplot(1, 2, 1)\n",
|
|
"\n",
|
|
"energy = 4.5\n",
|
|
"EIdx = np.argmin(np.abs(E-energy))\n",
|
|
"ax0.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_obs[:,:,EIdx],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax0.axis('equal')\n",
|
|
"ax0.set_xlabel('H [r.l.u.]')\n",
|
|
"ax0.set_ylabel('L [r.l.u.]')\n",
|
|
"ax0.set_title('Observations')\n",
|
|
"\n",
|
|
"# Plot 2: Subtracted Y\n",
|
|
"ax1 = fig0.add_subplot(1, 2, 2)\n",
|
|
"\n",
|
|
"energy = 4.5\n",
|
|
"EIdx = np.argmin(np.abs(E-energy))\n",
|
|
"p = ax1.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_sub[:,:,EIdx],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax1.axis('equal')\n",
|
|
"ax1.set_xlabel('H [r.l.u.]')\n",
|
|
"ax1.set_ylabel('L [r.l.u.]')\n",
|
|
"ax1.set_title('Subtracted signal')\n",
|
|
"\n",
|
|
"fig0.colorbar(p)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "da0f7346",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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|
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"text/plain": [
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"<Figure size 2000x900 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
|
|
],
|
|
"source": [
|
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"fig0,axx = plt.subplots(figsize=(20, 9),ncols=2,nrows=1)\n",
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"\n",
|
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"# Plot 1: Observations Y\n",
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"\n",
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"ax0 = axx[0]\n",
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"\n",
|
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"h_value = 0.25\n",
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"HIdx = np.argmin(np.abs(h-h_value))\n",
|
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"ax0.pcolormesh(L[:,HIdx,:],E[:,HIdx,:],Y_obs[:,HIdx,:],vmin=-2,vmax=50)\n",
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"\n",
|
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"ax0.set_xlabel('H [r.l.u.]')\n",
|
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"ax0.set_ylabel('L [r.l.u.]')\n",
|
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"ax0.set_title('Observations')\n",
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"\n",
|
|
"# Plot 2: Subtracted Y\n",
|
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"ax1 = axx[1]\n",
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"\n",
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"\n",
|
|
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"p = ax1.pcolormesh(L[:,HIdx,:],E[:,HIdx,:],Y_sub[:,HIdx,:],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax1.set_xlabel('L [r.l.u.]')\n",
|
|
"ax1.set_ylabel('E [meV]')\n",
|
|
"ax1.set_title('Subtracted signal')\n",
|
|
"\n",
|
|
"\n",
|
|
"fig0.colorbar(p,ax =axx, shrink=0.2, location='bottom')\n",
|
|
"\n",
|
|
"#plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "71d4ed7e-dec7-4c84-842a-d03030e42db3",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Compare the signal with the subtracted signal in a 1D cut"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "12829c7d-fbfd-4e4e-b3a1-c759d83f834e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig0 = plt.figure()\n",
|
|
"\n",
|
|
"# Plot 1: Observations Y\n",
|
|
"ax0 = fig0.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"h_value = 0.25\n",
|
|
"e_value = 5.5 # meV\n",
|
|
"EIdx = np.argmin(np.abs(e-e_value))\n",
|
|
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"ax0.scatter(L[:,HIdx,EIdx],Y_obs[:,HIdx,EIdx],label='Observations')\n",
|
|
"ax0.scatter(L[:,HIdx,EIdx],Y_sub[:,HIdx,EIdx],label='Subtracted Signal')\n",
|
|
"ax0.scatter(L[:,HIdx,EIdx],Y_back[:,HIdx,EIdx],label='Background')\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"ax0.set_xlabel('L [r.l.u.]')\n",
|
|
"ax0.set_ylabel('I [Arb. unit]')\n",
|
|
"ax0.legend()\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|